Urban planning is undergoing a transformation, driven by the ever-expanding capabilities of data-driven technologies. In Barcelona, where the Superblock model has sparked intense debates and discussions, the integration of data-driven approaches is reshaping the way policymakers and stakeholders make critical decisions. In this article, we explore how data-driven tools are being adopted today in urban contexts, offering a pathway to more effective and sustainable urban transformations.

To establish effective intervention criteria for Superblocks, gathering data on public spaces, pedestrian occupancy, traffic, and mobility has become essential. The municipality of Barcelona has already implemented various data collection solutions [1], but with emerging technologies, new data-driven tools have emerged, offering diverse approaches to uncover urban dependencies, identify patterns, and reveal implications through data-based observation and analysis.

The Integration of Data-Driven Tools

The integration of data-driven tools with computational methods provides a powerful means to gain a comprehensive understanding of urban dynamics. Physical data gathered from urban environments plays a pivotal role in informing urban intervention decisions.

Data serves as a valuable instrument for calibrating, comparing, and validating parameters derived from the usage of physical spaces, triggering the implementation of tools able to describe traffic and mobility [2], facilitate citizen participation [3], or even adjust digital simulations [4].
Especially, in regards to mobility, a crucial topic for the Superblocks model in Barcelona, harnessing this wealth of traffic data paves the way for precise travel time measurements to be obtained, empowering decision-makers with accurate insights.

The Role of Intrusive Sensors

In Barcelona, the extensive use of intrusive sensors, such as inductive loop detectors, has played a pivotal role in urban planning.

Inductive loop detectors (ILD), as described by Singh, Vanajakashi, and Tangirala, were initially introduced at the beginning of the 1960s and have become one of the most popular traffic detectors due to their widely extended technology and simple mode of operation [5]. These detectors are available in various configurations, dimensions, and shapes to suit different application contexts, whether installed underneath or on top of the road surface. Typically, a wire is used to form several loops, which, while not affecting the sensitivity of the signal provided, offers an advantage in terms of signal reception.

ILDs operate based on the principle of mutual inductance, detecting vehicles by measuring the change in inductance caused by their presence on top of the sensor. This change in voltage, known as the vehicle signature, serves as the raw output from the detector system.

RADAR, GPS, and RFIDs

In the framework of urban data collection, it is possible to identify another typology of a non-intrusive set of sensor technology. Non-intrusive sensors are kept over the roads or to the side of the roads. This category includes ultrasonic, infrared, microwave radar, and acoustic sensors.

Ultrasonic sensors release sound energy at frequencies ranging from 25 to 50 kHz, allowing them to recognize both pedestrians and automobiles within their transmitter’s range. They are known for their cost-effectiveness and straightforward hardware. However, it’s important to note that ultrasonic sensors have a fundamental disadvantage; they can be influenced by external factors such as environmental sounds and noise [6].

Radio Detection and Ranging (RADAR) works on the principle of Doppler’s effect as the moving body approaches nearer, frequency increases; and as it goes away, the frequency decreases. The radar gun targets directly towards any vehicle and the device displays the speed on it [7].

GPS technology revolutionized navigation and location-based services. The launch of the low-Earth orbit satellite, Sputnik 1, by the USSR (Soviet Union) on October 4, 1957 may possibly mark the beginning of Global Positioning System (GPS) technology. Scientists studying the low-Earth orbit of this early satellite realized they could track the satellite by the relative strength of its radio signal [8]. In transportation, GPS enables vehicle tracking, navigation systems, and real-time traffic updates, reducing travel time and improving efficiency.

RFID technology plays a crucial role in object identification and tracking. RFID tags, whether passive or active, enhance location accuracy and overall effectiveness. Equipping vehicles with RFID readers and strategically arranging RFID tags can significantly improve location tracking.

Limitations

The technologies integrated to this date offer a reduced range of solutions. Radars and traffic loops have been implemented in several streets to monitor traffic, offering nevertheless unclassified representation of mobility dynamics, generating data assets ignoring street morphologies, traffic lanes and classifications of vehicles, therefore providing limited data in terms of an actual representation of more complex mobility estimations, carbon emissions and deeper traffic analysis of mobility infrastructure, as you can see in the open data platform offered by Barcelona(Open Data Barcelona 2022).

As underlined by other studies, conducted by Ayaz and other data scientists, the ILD technology presents other types of limitations. “These sensors have significant limitations in heterogeneous traffic, such as in congested areas, and they are unable to count pedestrians” [6].

RADAR technology, while accurate for measuring vehicle speed, is limited in its ability to provide detailed visual information [9]. It excels in speed measurement but falls short in offering a comprehensive view of traffic patterns. RFID technology, although valuable for object identification and tracking, has limitations in terms of the range at which it operates. Passive RFID tags rely on nearby readers for power and communication, limiting their tracking capabilities in larger areas.

While RADAR and RFID technologies offer efficiency, emerging data analytics mediums are set to define novel standards for describing spatial dynamics. These advanced tools provide deeper insights into spatial phenomena, offering qualitative and quantitative analytical descriptions of usage patterns.

Conclusion

As the Superblock model continues to evolve, the integration of data-driven approaches is becoming increasingly pivotal. The wealth of data collected from urban environments equips policymakers and stakeholders with the necessary tools to make informed decisions that optimize efficiency and enhance the urban experience. In Barcelona and cities worldwide, data-driven urban planning is ushering in a new era of sustainable, effective, and evidence-based interventions. It is through these innovations that we can truly shape the cities of the future.

In the following chapters of this series, we will delve into the rising importance of image analytics and image processing via computer vision and machine learning. These cutting-edge technologies hold the potential to provide richer, more comprehensive data for urban planning. Stay tuned to discover how these innovations are shaping the future of Superblock interventions and urban planning as a whole.

References

1. Monge, F., Barns, S., Kattel, R., & Bria, F. (2022). A new data deal: The case of Barcelona. UCL Institute for Innovation and Public Purpose, (No. WP 2022/02). https://www.ucl.ac.uk/bartlett/public-purpose/publications/2022/feb/new-data-deal-case-barcelona
2. Martínez-Díaz, Margarita. 2022. ‘Accurate, Affordable and Widely Applicable Freeway Travel Time Prediction: Fusing Vehicle Counts with Data Provided by New Monitoring Technologies’. In The Evolution of Travel Time Information Systems: The Role of Comprehensive Traffic Models and Improvements Towards Cooperative Driving Environments, edited by Margarita Martínez-Díaz, 101–38. Springer Tracts on Transportation and Traffic. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-89672-0_4.
3. Monge, F., Barns, S., Kattel, R., & Bria, F. (2022). A new data deal: The case of Barcelona. UCL Institute for Innovation and Public Purpose, (No. WP 2022/02). https://www.ucl.ac.uk/bartlett/public-purpose/publications/2022/feb/new-data-deal-case-barcelona
4. Argota Sánchez-Vaquerizo, Javier. 2022. ‘Getting Real: The Challenge of Building and Validating a Large-Scale Digital Twin of Barcelona’s Traffic with Empirical Data’. ISPRS International Journal of Geo-Information 11 (1): 24. https://doi.org/10.3390/ijgi11010024.
5. Singh, Niraj Kumar, Lelitha Vanajakashi, and Arun K. Tangirala. 2018. ‘Segmentation of Vehicle Signatures from Inductive Loop Detector (ILD) Data for Real-Time Traffic Monitoring’. In 2018 10th International Conference on Communication Systems & Networks (COMSNETS), 601–6. https://doi.org/10.1109/COMSNETS.2018.8328281.
6. Ayaz, Shehzad, Hussain Ahmad, Hamza Khan, and Yasir Mehmood. 2021. ‘TRAFFIC FLOW MONITORING FOR HETEROGENEOUS TRAFFIC’ 3 (August): 54–61.
7. Jia, Y., Guo, L., & Wang, X. (2018). 4 — Real-time control systems. In L. Deka & M. Chowdhury (Eds.), Transportation Cyber-Physical Systems (pp. 81–113). Elsevier. https://doi.org/10.1016/B978-0-12-814295-0.00004-6
8. Kumar, S., & Moore, K. B. (2002). The Evolution of Global Positioning System (GPS) Technology. Journal of Science Education and Technology, 11(1), 59–80.
9. Belenguer, Ferran Mocholí, Antonio Martínez Millana, Antonio Mocholí Salcedo, and Victor Milián Sánchez. 2019. ‘Vehicle Modeling for the Analysis of the Response of Detectors Based on Inductive Loops’. PLOS ONE 14 (9): e0218631. https://doi.org/10.1371/journal.pone.0218631.

Data-Driven Urban Planning was originally published in Noumena Data on Medium, where people are continuing the conversation by highlighting and responding to this story.

The implementation of the Superblock model in Barcelona has been a topic of fervent debate, drawing contrasting responses from various stakeholders. While the Poblenou Superblock project has garnered significant media attention globally, it has not been without its share of criticisms. In this article, we delve into the multifaceted discourse surrounding the Barcelona Superblock model, drawing insights from various researchers and their studies.

Critiques and Concerns

Over the last decade, several researchers have highlighted the absence of a comprehensive evaluation of the potential and measurable effects of implementing the Superblock design on a city-wide scale. They emphasise the uncertainty surrounding how well the model adapts to different street networks, usage patterns, and urban structures. A key argument revolves around the lack of specific data to substantiate the proposed urban interventions within the Superblock. This has sparked a discussion around the approach to urban intervention, raising questions about the principles employed when selecting one street over another for Superblock implementation.

A study conducted by Zografos in the wake of the initial Superblock implementation in September 2016 highlighted several concerns. Critics raised questions about technical and organizational shortcomings, as well as a perceived lack of citizen involvement during the planning and design phases [1]. These early critiques shed light on the challenges of introducing such a transformative urban model.

Salvador Rueda, a prominent advocate for Superblocks, has argued that their implementation would significantly reduce air pollution levels, leading to improved environmental conditions [2]. Nevertheless, lingering questions persist about the analytical approach required to drive Superblock adoption and how to rigorously estimate its impacts.

Frago and Graziano’s study [3] adds another layer to the discussion by focusing on the selection criteria for Superblock implementation. They underline the Plataforma d’Afectats per la Superilla del Poblenou’s (PASP9) perspective, emphasizing the discrepancy between stated objectives and outcomes in the chosen Poblenou area.

PASP9 represents a diverse range of community members, including residents, business owners, and other stakeholders in the Poblenou area. Their mission is to advocate for the rights and concerns of those directly affected by the Superblock initiative in Poblenou. The platform was formed in response to the city’s decision to implement the Superblock model in this specific neighbourhood.

The platform’s primary stance revolves around the careful consideration of the Superblock’s objectives and outcomes, particularly in the context of Poblenou. They have raised valid concerns about the suitability of Poblenou as a test case for this urban planning experiment. These concerns primarily stem from the fact that Poblenou may not share the same levels of pollution and congestion as other parts of Barcelona, such as the Gracia district.

These findings raise valid concerns about the model’s applicability to different urban contexts, prompting the importance of employing data-driven criteria to accurately estimate the impact of such interventions into the urban context of Barcelona.

Addressing Data Gaps

In fact, several research studies underscore the importance of utilizing data to simulate and predict traffic patterns within Superblocks. However, it is essential to acknowledge that restricting traffic in one area can have ripple effects throughout the city’s street network, creating complexities in implementation [3].

Despite this, there is a notable scarcity of research that effectively compares digital simulations with real-world data, which is vital for validating conclusions.

Eggimann, in his article [4], highlights the limited attention Superblocks have received in research, despite their potential benefits. He also calls for systematic quantification of the potential for Superblock designs in different cities, emphasizing the need for context-specific analysis.

Podjapolskis [5] points out concerns regarding the rigour of data used to justify Superblock applications. Additionally, he highlights a contradiction in reducing vehicle speeds while potentially increasing emissions. These issues underscore the need for more comprehensive and accurate data in Superblock planning and evaluation.

Conclusion

In conclusion, the implementation of the Barcelona Superblock model has sparked intense debates and discussions among researchers, policymakers, and the public. While some herald its potential benefits, others raise valid concerns about its adaptability and the lack of concrete data to support proposed interventions.

The ongoing discourse emphasizes the need for data-driven criteria and methodologies to accurately estimate the impact of Superblock interventions. As we move forward, it is clear that data will play a pivotal role in shaping the future of urban planning and the success of Superblocks in Barcelona and beyond. In the next chapters, we will delve deeper into how data can serve as a cornerstone for this new urban development paradigm.

REFERENCES

1. Zografos, Christos, Kai A. Klause, James J. T. Connolly, and Isabelle Anguelovski. 2020. ‘The Everyday Politics of Urban Transformational Adaptation: Struggles for Authority and the Barcelona Superblock Project’. Cities 99 (April): 102613. https://doi.org/10.1016/j.cities.2020.102613.
2. Rueda, Salvador. 2011. ‘Las supermanzanas: reinventando el espacio público, reinventando la ciudad’. In Ciudades (im)propias: la tensión entre lo global y lo local, 2011, ISBN 978–84–694–2906–8, págs. 123–134, 123–34. Centro de Investigación Arte y Entorno. https://dialnet.unirioja.es/servlet/articulo?codigo=3829343.
3. Frago, Lluis, and Teresa Graziano. 2021. ‘Public Space and the Green City: Conflictual Narratives of the Superblock Programme in Poblenou, Barcelona’. Journal of Urban Regeneration and Renewal, September. https://hstalks.com/article/6618/public-space-and-the-green-city-conflictual-narrat/.
4. Eggimann, Sven. 2022. ‘The Potential of Implementing Superblocks for Multifunctional Street Use in Cities’. Nature Sustainability 5 (5): 406–14. https://doi.org/10.1038/s41893-022-00855-2.
5. Podjapolskis, Robert. 2017. ‘Supermanzanas bajo sospecha: el proyecto de Superislas para Ensanche frente a su alternativa’. Seminario Internacional de Investigación en Urbanismo, no. 9. https://doi.org/10.5821/siiu.6388.

Evaluating Superblocks was originally published in Noumena Data on Medium, where people are continuing the conversation by highlighting and responding to this story.

The geological era of the Anthropocene is ushering in a new shift in weather patterns affecting climate, biodiversity, and geomorphology, ultimately impacting our cities and built environments. Today, urban planners and administrators are faced with the urgent need to adapt our built environments to the rising levels of air contamination, an invisible enemy that is deteriorating the well-being of our entire population.

Numerous studies have linked air pollution to congenital anomalies, particularly exposure to traffic-related gases. The Air Quality Index (AQI) categorizes the risks associated with air pollutants based on different scales, with warnings beginning at 51–100 and levels between 201–300 causing a significant increase in respiratory effects, while levels above 301–500, could translate into serious heart or lung disease or even premature mortality.

To face the unprecedented threat triggered by pollution, cities must reprogram their urban infrastructures and reshape their urban patterns towards safer, healthier, and more ecological solutions. The extension plan proposed by Ildefonso Cerdà for the city of Barcelona was designed to balance lighting, ventilation, public spaces, greenery, and mobility into a hybrid urban configuration. However, Barcelona is today one of the most polluted cities in Europe, partly due to its geography, high traffic density, and a large proportion of diesel-powered vehicles.

In 2016, Barcelona introduced a new urban planning model called the Superblock, which aims to reduce motorized transportation, promote sustainable mobility and active lifestyles, provide urban greening, and mitigate the effects of climate change. This planning approach regulates access to cars and vehicles in public streets while enabling the extension of walkable areas and cycling paths, threatening the urban fabric as a programmable surface.

Along with the development of new urban strategies, novel data-driven instruments have emerged, providing different approaches to inform spatial planning. This article highlights several techniques triggered by computer vision and machine learning algorithms to analyze image-based data and extract meaningful metrics to inform spatial transformations and estimate CO2 emissions in an urban environment.

Towards Spatial Analytics

As technology continues to advance and expand, it is becoming increasingly important to establish decision-making protocols that prioritize resilient, participatory, and responsive public spaces. In urban environments, this means embracing the emerging opportunities presented by big data analytics and urban informatics to determine metrics and transform urban spaces.

Big data represents a wide spectrum of observational and informal data produced through a variety of activities in the urban environment. Urban informatics is the operational area dedicated to exploring and understanding urban systems by leveraging novel sources of data. This emerging field has tremendous potential to improve dynamic urban resource management, drive theoretical insights and knowledge discovery of urban patterns and processes, increase urban engagement and civic participation, and facilitate innovations in urban management, planning, and policy analysis.

Within the realm of big data analytics, image-based data analytics has become increasingly important. Machine learning and computer vision algorithms have enabled a variety of image-based applications, and image analytics has emerged as a disruptive technology to sense, capture, and describe complex spatial dynamics.

While mobile data has traditionally been the go-to source for spatial dynamics information, it presents limitations such as positioning inaccuracies and an inability to provide useful insights regarding transportation means or individual behaviours. Image analytics, on the other hand, can introduce new workflows and provide information-rich descriptions of behaviour that can support the development of novel design systems.

By generating datasets from video frames using computer vision and machine learning, convolutional neural networks can be used to classify video and image contents and provide valuable insights into spatial dynamics. This emerging technology holds great promise for informing design solutions through spatial-sensing data and enabling the development of an architecture that considers the interactions between occupants and space based on factual observations.

DEEP LEARNING AND SPATIAL ANALYTICS

The rapid increase of publicly available labelled data and the development of GPU computing has significantly enhanced the performance and efficiency of deep learning algorithms. Unsupervised training logic introduced by Hinton and LeCun in the 80s, and later on boosted by AlexNet, marked a major breakthrough, which subsequently led to the development of deep architectures and deep learning algorithms for computer vision applications, such as object detection, image recognition, motion tracking, pose estimation, and semantic segmentation. These algorithms have played a pivotal role in the emerging field of spatial analytics, with a wide range of applications such as Crowd Management, Public Space Design, Virtual Environments, Visual Surveillance, and Intelligent Environments.

In the context of the Superblock, the analysis of mobility and patterns of spatial occupancy are essential parameters for the activation of a new urban model based on restricted accessibility for cars and heavy vehicles. To create such a new public space, a mobility model needs to be developed to ensure accessibility, comfort, safety, and multi-functionality. Indeed, a data-driven protocol can provide citizens with the opportunity to exercise their rights to culture, leisure, expression, demonstration, and movement.

By leveraging these technologies, a responsive approach can be introduced to adapt, reconfigure, and extend public spaces, providing criteria for intervention to regulate mobility, and enabling the creation of more accessible and liveable public spaces.

In the coming articles, we will delve into the methodologies and solutions developed by the author and the Noumena team. Our focus will be on the application of advanced algorithms based on computer vision and machine learning to interpret image data and extract valuable insights from the actual usage of public spaces.

Acknowledgement

Noumena: Oriol Arroyo, Salvador Calgua, Maria Espina, Iacopo Neri, Paula Osés Noguero, Carlo Ciuffarelli

Sensing Superblocks was originally published in Noumena Data on Medium, where people are continuing the conversation by highlighting and responding to this story.